Soil moisture and surface roughness retrieval from multiparametric SAR to support water resources management in Mediterranean areas
Ralf Ludwig(2), Monique Bernier(1), Claudio Paniconi(1), Philip Marzahn(2), Antonino Soddu(3), Massimo Melis(3), Karsten Krueger(4) and Rainer Duttmann(4)
(1) INRS-ETE, 490 de la Couronne, G1K 9A9, Québec, QC, Canada
(2) University of Munich, Luisenstrasse 37, 80333 Munich, Germany
(3) Agricultural Research Agency of Sardinia, viale Trieste 111, 09123 Cagliari, Italy
(4) University of Kiel, Ludewig-Meyn-Str. 14, 24098 Kiel, Germany
Mediterranean countries are at high risk for an even pronounced susceptibility to water stress and drought, which is expected to have severe direct impact on agricultural productivity. The presented project is aiming to conjointly employ field monitoring, spaceborne SAR and hydrologic modelling to support adaptive water resources management and best agricultural practice. These efforts tie in with ongoing research on data assimilation, as periodic observations of surface soil moisture can be used to update the land surface boundary conditions that drive surface and subsurface partitioning of water and energy fluxes in a hydrological model.
The study is conducted in the Campidano plain, the agricultural heartland of Sardinia (Italy). The Azienda St. Michele is operated by the regional agricultural research agency AGRIS and disposes of an extensive data base of continuous field data and satellite imagery. The scientific objectives of this international project initiative are separated into four tasks:
a) development of an improved soil moisture and roughness inversion algorithm from polarimetric and dual-polarized PALSAR-data for annual crops and grassland.
b) adaptation of an available distributed and physically based hydrologic model for an assimilation of spatial soil moisture quantities derived from a)
c) evaluation of model results prior and after data assimilation and assessment of uncertainties related to the retrieval algorithm and model concept
d) implementation of the adjusted model into an Integrated Watershed Management strategy for the Campidano plain under climate and land use change impacts.
A series of PALSAR and ASAR image acquisitions from 2007 and 2008 are available for the test area. These were accompanied by intense field campaigns for soil physical and vegetation parameters. Data and findings from these campaigns (3 bare fields, 3 crop fields) are used to develop and validate an empirical model for the inversion of surface soil moisture and roughness from ASAR images and its adaptation to PALSAR data.
In a first qualitative assessment, Level 1.1 polarimetry mode PALSAR data was used for further image analysis. Results of an entropy/anisotropy/alpha decomposition as well as the Freeman-Durden decomposition are shown to highlight the great potential of PALSAR data compared to ASAR imagery to distinguish between bare soils and vegetated fields as well as between different crops based on various examined classification methods (scattering mechanisms, knowledge based and statistically driven).